Accounting for spatial sampling patterns in Bayesian phylogeography.
Statistical phylogeography provides useful tools to characterize and quantify the spread of organisms during the course of evolution. Analyzing geo-referenced genetic data often relies on the assumption that samples are preferentially collected in densely populated areas of the habitat. Deviation from this assumption negatively impacts the inference of the spatial and demographic dynamics. This issue is pervasive in phylogeography. It affects analyses that approximate the habitat as a set of discrete demes as well as those that treat it as a continuum. The present study introduces a new Bayesian modeling approach that explicitly accommodates for spatial sampling strategies. An original inference technique, based on recent advances in statistical computing, is then described that is most suited to modeling data where sequences are preferentially collected at certain locations, independently of the outcome of the evolutionary process. The analysis of geo-referenced genetic sequences from the West Nile virus in North America along with simulated data shows how assumptions about spatial sampling may impact our understanding of the forces shaping biodiversity across time and space.
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Stéphane Guindon, LIRMM - CNRS
I design probabilistic models of evolution and algorithms to infer their parameters from the analysis of molecular, fossil and/or spatial data. I created and am still developing the package PhyML (for Phylogenetics through Maximum Likelihood) which serves as a basis to implement my research outputs.
Trained as a biologist/statistician, I am working as a CNRS research scientist in the computer science department of the LIRMM in Montpellier, France. I was also lucky to work for the Department of Statistics at the University of Auckland between 2007 and 2015.